Best for
- User provides vague feature requests ("build a dashboard", "create a reminder app")
- Requirements lack specific conditions, triggers, or measurable outcomes
- Natural language descriptions need conversion to testable specifications
daymade/claude-code-skills/prompt-optimizer/SKILL.md
Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax) methodology. This skill should be used when users provide loose requirements, ambiguous feature descriptions, or need to enhance prompts for AI-generated code, products, or documents. Triggers include requests to "optimize my prompt", "improve this requirement", "make this more specific", or when raw requirements lack detail and structure.
Decision brief
Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax) methodology. This skill should be used when users provide loose requirements, ambiguous feature descriptions, or need to enhance prompts for AI-generated code, products, or documents.
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/daymade/claude-code-skills --skill "prompt-optimizer"Inspect the Agent Skill "prompt-optimizer" from https://github.com/daymade/claude-code-skills/blob/57864f4cb98bc9b7f80c8793b507cebbd6d75efc/prompt-optimizer/SKILL.md at commit 57864f4cb98bc9b7f80c8793b507cebbd6d75efc. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Identify weaknesses: - Overly broad - "Add user authentication" → Missing password requirements, session management - Missing triggers - "Send notifications" → Missing when/why notifications trigger - Ambiguous actions - "Make it user-friendly" → No measurable usability criteria…
Identify weaknesses: - Overly broad - "Add user authentication" → Missing password requirements, session management - Missing triggers - "Send notifications" → Missing when/why notifications trigger - Ambiguous actions - "Make it user-friendly" → No measurable usability criteria…
Convert requirements to EARS patterns. See references/earssyntax.md for complete syntax rules.
Match requirements to established frameworks. See references/domaintheories.md for full catalog.
Generate specific examples with real data: - User scenarios: "When user logs in on mobile device..." - Data examples: "Product: 'Laptop', Price: $999, Stock: 15" - Workflow examples: "Task: Write report → Sub-tasks: Research (2h), Draft (3h), Edit (1h)"
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 94/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 1,348 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Optimize vague prompts into precise, actionable specifications using EARS (Easy Approach to Requirements Syntax) - a Rolls-Royce methodology for transforming natural language into structured, testable requirements.
Methodology inspired by: This skill's approach to combining EARS with domain theory grounding was inspired by 阿星AI工作室 (A-Xing AI Studio), which demonstrated practical EARS application for prompt enhancement.
Four-layer enhancement process:
Apply when:
Identify weaknesses:
Convert requirements to EARS patterns. See references/ears_syntax.md for complete syntax rules.
Five core patterns:
The system shall <action>When <trigger>, the system shall <action>While <state>, the system shall <action>If <condition>, the system shall <action>If <condition>, the system shall prevent <unwanted action>Quick example:
Before: "Create a reminder app with task management"
After (EARS):
1. When user creates a task, the system shall guide decomposition into executable sub-tasks
2. When task deadline is within 30 minutes AND user has not started, the system shall send notification with sound alert
3. When user completes a sub-task, the system shall update progress and provide positive feedback
Transformation checklist:
Match requirements to established frameworks. See references/domain_theories.md for full catalog.
Common domain mappings:
Selection process:
Generate specific examples with real data:
Examples must be realistic, specific, varied (success/error/edge cases), and testable.
Structure using the standard framework:
# Role
[Specific expert role with domain expertise]
## Skills
- [Core capability 1]
- [Core capability 2]
[List 5-8 skills aligned with domain theories]
## Workflows
1. [Phase 1] - [Key activities]
2. [Phase 2] - [Key activities]
[Complete step-by-step process]
## Examples
[Concrete examples with real data, not placeholders]
## Formats
[Precise output specifications:
- File types, structure requirements
- Design/styling expectations
- Technical constraints
- Deliverable checklist]
Quality criteria:
Output in structured format:
## Original Requirement
[User's vague requirement]
**Identified Issues:**
- [Issue 1: e.g., "Lacks specific trigger conditions"]
- [Issue 2: e.g., "No measurable success criteria"]
## EARS Transformation
[Numbered list of EARS-formatted requirements]
## Domain & Theories
**Primary Domain:** [e.g., Authentication Security]
**Applicable Theories:**
- **[Theory 1]** - [Brief relevance]
- **[Theory 2]** - [Brief relevance]
## Enhanced Prompt
[Complete Role/Skills/Workflows/Examples/Formats prompt]
---
**How to use:**
[Brief guidance on applying the prompt]
For complex scenarios, see references/advanced_techniques.md:
Do's: ✅ Break down compound requirements (one EARS statement per requirement) ✅ Specify measurable criteria (numbers, timeframes, percentages) ✅ Include error/edge cases ✅ Ground in established theories ✅ Use concrete examples with real data
Don'ts: ❌ Avoid vague language ("fast", "user-friendly") ❌ Don't assume implicit knowledge ❌ Don't mix multiple actions in one statement ❌ Don't use placeholders in examples
Load these reference files as needed:
references/ears_syntax.md - Complete EARS syntax rules, all 5 patterns, transformation guidelines, benefitsreferences/domain_theories.md - 40+ theories mapped to 10 domains (productivity, UX, gamification, learning, e-commerce, security, etc.)references/examples.md - Four complete transformation examples (procrastination app, e-commerce product page, learning dashboard, password reset security) with before/after comparisons and reusable templatereferences/advanced_techniques.md - Multi-stakeholder requirements, non-functional specs, complex conditional logic patternsWhen to load references:
ears_syntax.mddomain_theories.mdexamples.mdadvanced_techniques.mdFrequently asked questions
Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax) methodology. This skill should be used when users provide loose requirements, ambiguous feature descriptions, or need to enhance prompts for AI-generated code, products, or documents.
The source record exposes this install command: npx skills add https://github.com/daymade/claude-code-skills --skill "prompt-optimizer". Inspect the command and pinned source before running it.
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